Desired System Properties Research Articles

Top-down design of protein architectures with reinforcement learning.

As a result of evolutionary selection, the subunits of naturally occurring protein assemblies often fit together with substantial shape complementarity to generate architectures optimal for function in a manner not achievable by current design approaches. We describe a "top-down" reinforcement learning-based design approach that solves this problem using Monte Carlo tree search to sample protein conformers in the context of an overall architecture and specified functional constraints. Cryo-electron microscopy structures of the designed disk-shaped nanopores and ultracompact icosahedra are very close to the computational models. The icosohedra enable very-high-density display of immunogens and signaling molecules, which potentiates vaccine response and angiogenesis induction. Our approach enables the top-down design of complex protein nanomaterials with desired system properties and demonstrates the power of reinforcement learning in protein design.

Cyberinfrastructure for Social Good: Ensuring That No Homeless Individual Stays Behind

We present a prototype decentralized transactional platform designed to improve the transparency of homeless serving organizations and facilitate their accountability and oversight. In the proposed system, the complete history of transactions between organizations offering homelessness services (e.g., shelters, transitional housing) and individuals seeking such services is stored in a distributed ledger. Using smart contracts, the proposed tamper-proof framework can automate the exchange of information between clients, organizations and government agencies, and allow government agencies audit organizations without violating the privacy of homeless individuals. We begin by describing the goals and concepts, the stakeholders’ requirements and the corresponding desirable system properties, and identified challenges. We continue with an in-depth description of the overall architecture of the proposed system designed to achieve these goals, and lessons learned towards transitioning this system to the real-world.

Various diffraction effects and their importance for detection of inhomogeneites in human tissues

Hitherto described microwave modalities for detection of internal inhomogeneities in human tissues such as breasts and heads are by image reconstruction, requiring time-consuming computational resources. The method developed at MDH is instead based on the use of a magnetic field transducer, creating an essentially circular electrical field. This is in turn diffracted by the dielectric inhomogenity and that signal is received by an E-field sensor in an appropriate position. The transmitting applicator is unique by no need to contact the object under study (OUS) and does not generate any surface waves at it. The primary field has properties behaving as coming from a magnetic monopole. The receiving 3D contacting applicator contains a high-permittivity ceramic and is resonant in order to provide the desired field polarisation sensitivity. The desired system properties are achieved by optimized use of the orthogonality properties of the primary magnetic, induced electric, and diffracted electric fields. Key words: Diffraction, magnetic field, applicator, internal inhomogeneity.

Expressibility Required to Design Embedded and Real-time Systems

Embedded and real-time systems are characterised by safety criticality, reliability, real-time, limited resources, low-cost and re-usability, etc. Development of such systems is a complex engineering process, involving many different disciplines with respect to their demanding requirements, in order to achieve desired system properties and reduce the complexity of the development process, we compile these requirements and criteria for design, specification and modelling of embedded real-time systems, it is benefit for rapid development of high quality systems.

Functional Modeling View on Product and Process Engineering in Design and Operations

Recent significant developments within process systems engineering demonstrate a need for more-flexible tools to handle the many diverse model-based tasks within product and process synthesis, design, innovation, and operation. Consequently, it is relevant to have a closer look at one of the modeling methodologies that have emerged. This paper addresses functional modeling for the purpose of the object system under investigation. The purpose of functional modeling is to represent the relationsships between the system goal and the underlying phenomena. The role of different aspects of functional modeling within the ongoing development of process systems engineering is analyzed. The fundamental principles of functional modeling for the object under investigation are presented and functional modeling hermeneutics are defined as translating the modeling goal into desired system properties, identifying the theory needed to model the system to represent these properties and identifying criteria for evaluation o...

HARD-SOFT TECHNOLOGY OF INFORMATION SUSPENSION PROCESS OF MODELING OF HEAT GENERATION/HEAT CONSUMPTION IN THE INTERNAL COMBUSTION ENGINE

Deterministic and, in a certain sense, "linear" interpretation of the world often leads to the recognition of the fact that the more accurate model we need, the more complex it must be (as in case of a formalized reproduction of the real system, or the implementation of the desired system properties in the process of formal synthesis of something new). Instead, following the principle of synergy leads to the conviction that there is always a certain model of optimal complexity e.g. in the synthesis of the new system, and in the analysis of real system peculiarities. However, the model of reality could be a part of this reality that is included to the carefully structured formal description. Since we cannot penetrate into the working space of the serial engine while testing, we should use a test engine of a special construction when the working space corresponds to the laws of similarity and this engine will serve as a model of the working space of the serial engine.
 
 
 The study illustrates the effectiveness of hard-soft technology while investigating the peculiarities of heat generation and heat consumption in the internal combustion engine, which will combine mathematic and algorithmic means of modelling as well as the means of real simulation. The necessity of hard-soft technology introduction arises from the excessive complexity of thermal phenomena occurring in the internal combustion engine (ICE), and the inability to fully subordinate these phenomena to existing analytical models.
 The combination of original and analytical properties, reality and virtual reality while modelling the processes in internal combustion engines allows us to substantially improve the quality of information in the process of design and engine construction. Taking this into consideration, there are some natural grounds to apply principles of heuristic self-organization, self-learning, means of the neural networks, etc. in the design implementation.
 The study demonstrates the example of modelling the real working space of ICE with the forced start that serves as a supplement to the mathematical algorithmic two-zone model of heat generation / heat consumption / heat extraction.
 The basic information that can be obtained by means of hard-soft technology in the framework of, for example, the two-zone model of the work process in the gasoline engine, is the variability with the change in the angle of rotation of the crankshaft of the engine: absolute pressure (indicative diagram); absolute temperature; heat transmitted inside the cylinder between zones; coefficient of excess air; coefficient of heat transfer; intensity of heat extraction in the process of combustion of fuel; intensity of heat transfer through the walls of the cylinde

Tractable stochastic analysis in high dimensions via robust optimization

Modern probability theory, whose foundation is based on the axioms set forth by Kolmogorov, is currently the major tool for performance analysis in stochastic systems. While it offers insights in understanding such systems, probability theory, in contrast to optimization, has not been developed with computational tractability as an objective when the dimension increases. Correspondingly, some of its major areas of application remain unsolved when the underlying systems become multidimensional: Queueing networks, auction design in multi-item, multi-bidder auctions, network information theory, pricing multi-dimensional options, among others. We propose a new approach to analyze stochastic systems based on robust optimization. The key idea is to replace the Kolmogorov axioms and the concept of random variables as primitives of probability theory, with uncertainty sets that are derived from some of the asymptotic implications of probability theory like the central limit theorem. In addition, we observe that several desired system properties such as incentive compatibility and individual rationality in auction design are naturally expressed in the language of robust optimization. In this way, the performance analysis questions become highly structured optimization problems (linear, semidefinite, mixed integer) for which there exist efficient, practical algorithms that are capable of solving problems in high dimensions. We demonstrate that the proposed approach achieves computationally tractable methods for (a) analyzing queueing networks, (b) designing multi-item, multi-bidder auctions with budget constraints, and (c) pricing multi-dimensional options.

An advanced algorithm for partitioning and parameter estimation in local model networks and its application to vehicle vertical dynamics

In this paper, advanced concepts for the identification of complex nonlinear systems are discussed. Three major problems are addressed: The nonlinearity of the system, noise in the data upon which the model has to be built, and the potential to incorporate qualitative and quantitative prior knowledge about the system. As an integrated solution approach, local model networks (LMNs) with appropriate parameter estimation schemes are proposed. LMNs generally offer a versatile structure for the identification of nonlinear dynamic systems. In order to account for a realistic situation when noise is present both in input and output data, an equality constrained generalised total least squares algorithm for the local model parameter estimation of the LMN is presented; the incorporation of equality constraints allows to mathematically enforce desired system properties. As an application and benchmark problem, the vertical dynamics of a vehicle is considered. After training the LMN on a rough road, excellent predictions of the behaviour of the vehicle at crossing a single obstacle are obtained, thus proving the effectiveness of the proposed algorithm. It is illustrated how both the application of a proper parameter estimation scheme and the integration of system constraints systematically improve the performance of the model.

Learning and Representing Temporal Knowledge in Recurrent Networks

The effective integration of knowledge representation, reasoning, and learning in a robust computational model is one of the key challenges of computer science and artificial intelligence. In particular, temporal knowledge and models have been fundamental in describing the behavior of computational systems. However, knowledge acquisition of correct descriptions of a system's desired behavior is a complex task. In this paper, we present a novel neural-computation model capable of representing and learning temporal knowledge in recurrent networks. The model works in an integrated fashion. It enables the effective representation of temporal knowledge, the adaptation of temporal models given a set of desirable system properties, and effective learning from examples, which in turn can lead to temporal knowledge extraction from the corresponding trained networks. The model is sound from a theoretical standpoint, but it has also been tested on a case study in the area of model verification and adaptation. The results contained in this paper indicate that model verification and learning can be integrated within the neural computation paradigm, contributing to the development of predictive temporal knowledge-based systems and offering interpretable results that allow system researchers and engineers to improve their models and specifications. The model has been implemented and is available as part of a neural-symbolic computational toolkit.

Automatic Generation of Computer Models through the Integration of Production Systems Design Software Tools

The design of production and logistic systems is a process of managing both technical and organizational variants in order to identify the best solution for a given system. This is a very well-known industrial engineering issue, where the objectives for designing such a system have been changing over the last decades. Former approaches were concerned about material handling costs only but more recent works include re-layout and product mix costs, together with a great concern on processes – high service levels, optimal scheduling policies, setup times and costs, etc. Nowadays, the rapid technological progress and the associated competitive problems lead to a great need of fast and successful solutions to deal with continuous change (re-design) of the currently used industrial systems. Flexibility, modularity, efficiency and robustness are generally highly desired system properties. For general design of industrial systems, three basic types of software tools are used: Computer Aided Design, Simulation and Information Systems. These tools help on improving the utilization of system resources like equipment, manpower, materials, space, energy, information, etc. Nevertheless these three types of software tools have been used with low levels of integration. This absence of an adequate data connection and integration of outputs cause time delays in the design process, duplication of work and could also be a source of errors. In this work, Production Systems Design software tools integration possibilities are discussed and a unified system architecture solution, implemented on AutoCAD (layout design), Witness (Simulation) and MS-Access (Information Systems) is presented. The aim is to focus on the need of data coherence between different software tools, exploring ways of dealing with data diversity and assuring valid and efficient solutions. MS-Access supports the specification of the system and data exchange between Witness and AutoCAD. Based on the database specification, our application automatically generates simulation programs and also different spatial patterns of project layouts. These tasks are implemented in Visual Basic code. Iteratively the results from the simulations are used to improve AutoCAD layouts and AutoCAD layouts are used in new simulations. The use of our application, in the examples showed in this paper, proved to get quick, valid and efficient solutions.

Aspect-Oriented Modeling and Verification with Finite State Machines

Aspect-oriented programming modularizes crosscutting concerns into aspects with the advice invoked at the specified points of program execution. Aspects can be used in a harmful way that invalidates desired properties and even destroys the conceptual integrity of programs. To assure the quality of an aspect-oriented system, rigorous analysis and design of aspects are highly desirable. In this paper, we present an approach to aspect-oriented modeling and verification with finite state machines. Our approach provides explicit notations (e.g., pointcut, advice and aspect) for capturing crosscutting concerns and incremental modification requirements with respect to class state models. For verification purposes, we compose the aspect models and class models in an aspect-oriented model through a weaving mechanism. Then we transform the woven models and the class models not affected by the aspects into FSP (Finite State Processes), which are to be checked by the LTSA (Labeled Transition System Analyzer) model checker against the desired system properties. We have applied our approach to the modeling and verification of three aspect-oriented systems. To further evaluate the effectiveness of verification, we created a large number of flawed aspect models and verified them against the system requirements. The results how that the verification has revealed all flawed models. This indicates that our approach is effective in quality assurance of aspect-oriented state models. As such, our approach can be used for model-checking state-based specification of aspect-oriented design and can uncover some system design problems before the system is implemented.

Model checking of healthcare domain models

This paper shows the application of a type of formal software verification technique known as lightweight model checking to a domain model in healthcare informatics in general and public health surveillance systems in particular. One of the most complex use cases of such a system is checked using assertions to verify one important system property. This use case is one of the major justifications for the complexity of the domain model. Alloy Analyzer verification tool is utilized for this purpose. Such verification work is very effective in either uncovering design flaws or in providing guarantees on certain desirable system properties in the earlier phases of the development lifecycle of any critical project.

Dependability through Assured Reconfiguration in Embedded System Software

In many software systems, properties necessary for dependable operation are only a small subset of all desirable system properties. Assuring properties over the simpler subset can provide assurance of critical properties over the entire system. This work provides a method for constructing systems to be dependably reconfigurable. A system's primary function can have less demanding dependability requirements than the overall system because the system can reconfigure to some simpler function. Reconfiguration thus controls the effective complexity of the system without forcing that system to sacrifice desired, but unassurable, capabilities. Focusing a system's dependability argument on reconfiguration means that reconfiguration must proceed correctly with very high assurance. The system construction approach in this work also provides a method through which system dependability properties can be shown. To illustrate the ideas in this work, we have built part of a hypothetical avionics system that is typical of what might be found on an unmanned aerial vehicle

High Assurance Software Systems

The last few decades are marked by an unprecedented increase in the complexity and consequence of information technology systems. High assurance software systems must satisfy basic functional service properties that the system intends to deliver, as well as guarantee desirable system properties such as security, safety, timeliness and reliability. Examples of high assurance software systems include command and control systems, nuclear power plants, electronic banking, aerospace systems, automated manufacturing and medical systems. One of the major challenges in high assurance software engineering is to develop well-founded methods for system construction, verification and validation, so they provide critical services with a high degree of confidence in their correctness and quality. The goal of the special issue is to bring together innovative research ideas and advances in the field of high assurance software systems to address these challenges. To this end, the guest editors invited six papers published in the 29th IEEE Annual International Computer Software and Applications Conference (COMPSAC 2005) in the conference theme of High Assurance Software Systems to submit to this special issue, from which four papers after a rigorous review process have been selected to appear in the special issue. In their paper ‘An Empirical Performance Study on PSIM’, Jinchun Xia, Carl K. Chang, Jeff Wise and Yujia Ge [1] address performance evaluation of software architecture designs. They describe a PSIM (Performance SImulation and Modeling) tool that integrates performance properties into software architecture specifications expressed in UML syntax. PSIM models can be transformed into Colored GSPN (Colored Generalized Stochastic Petri nets) via an automated tool. As a result, the Colored GSPN models can be simulated to perform model-based performance evaluation. PSIM is shown to be effective in predicting system performance and identifying system performance bottlenecks to address the timeliness property of high assurance software systems early in the development lifecycle. High assurance mobile computing systems often have fault tolerance and timeliness requirements which exceed those encountered in conventional distributed systems. The second paper ‘Design and Evaluation of a Low-Latency Checkpointing Scheme for Mobile Computing Systems’ by Guohui Li and Lihchyun Shu [2] presents a coordinated checkpointing scheme which reduces the latency in global checkpointing for failure recovery of high assurance mobile systems. The method proposed significantly reduces the latency associated with checkpoint request propagation. Experimental results indicate that an improvement of up to a 60% reduction in latency can be achieved with <2% extra overhead incurred during runtime to provide timely failure recovery for high assurance mobile systems. The third paper ‘A Blocking-Based Approach to Protocol Validation’ by Qizhi Ye, Yu Lei and David Kung [3] reports a new analysis method for protocol validation. Their method, based on reachability analysis, guarantees to detect protocol logical errors including deadlocks, unspecified receptions and channel overflows prior to actual system deployment. The authors apply the analysis method to 160 synthesized protocols as well as two real protocols to demonstrate its applicability to high assurance software systems that rely on these protocols. Its performance is especially sound when the non-blocking ratio of a protocol is high. Phishing is a form of online identity theft that aims to steal sensitive information such as online banking passwords and credit card information from users. The last paper ‘Protecting Users against Phishing Attacks’ by Engin Kirda and Christopher Kruegel [4] presents a novel method that protects users against spoofed web-based phishing attacks. Their tool called AntiPhish tracks the sensitive information of a user and generates warnings whenever the user attempts to transmit this information to a website that is considered untrusted. AntiPhish is a step in the right direction for protecting software systems against spoofed website-based phishing attacks. We would like to thank the reviewers for their superb work of refereeing and improving the quality as well as the presentation of the four papers selected in this special issue.

Formal Verification Analysis of Load-Voltage Power Control

Abstract This paper introduces an application of a Formal Verification (or model checking) technique to improve performance in a voltage collapse power control system. The model of the control system has a hybrid character and is comprised of a continuous-time load dynamics model, algebraic equations of the power balance on the load bus, and a discrete tap changer controller. The model is developed by ABB Schweiz AG Corporate Research as a test case intended for control synthesis and analysis of hybrid systems. Formal Verification of hybrid control system relies on the possibility to construct a (mite state transition model of a discretized system. An efficient search procedure is used to check whether desired system properties hold true in that model. Formal verification methods provide full coverage of all possible cases (scenarios), and thereby check whether the specified system properties are satisfied. The power control system properties were verified with the help of RuleBase, a formal verification ...

What has requirements research ever done for us? (goal-modeling techniques)

Requirements research has produced two principal goal-modeling techniques-knowledge acquisition in automated specification (KAOS) and i*. Both investigate goals that can provide criteria for determining whether requirements are relevant and complete. KAOS treats goals as desired system properties that traditional requirements-gathering techniques discover. It structures them into directed acyclic graphs. The i* approach models information systems as heterogeneous actors with different, often competing goals that nonetheless depend on each other to undertake their tasks and achieve these goals.

An object-oriented simulation framework for real-time control of automated flexible manufacturing systems

This paper describes an object-oriented simulation approach for the design of a flexible manufacturing system that allows the implementation of control logic during the system design phase. The object-oriented design approach is built around the formal theory of supervisory control based on Finite Automata. The formalism is used to capture inter-object relationships that are difficult to identify in the object-oriented design approach. The system resources are modeled as object classes based on the events that have to be monitored for real-time control. Real-time control issues including deadlock resolution, resource failures in various modes of operation and recovery from failures while sustaining desirable logical system properties are integrated into the logical design for simulating the supervisory controller.

Software in the year 2010

In 2010, it is safer because software technologies provide protections. However, the world has also become more dangerous because the atmosphere of mutual trust that was the basis of the early Internet has been largely obliterated. In the peer sets of 2010, each member of a peer set has its own internal model of what its environment is supposed to look like. The infamous Spyware Debacle of 2005 is long past. As a discipline, software engineering in 2010 has changed in some rather striking ways, although some might argue that it has simply returned to its roots. Outsourcing of both the private and the open-source varieties has removed most of the more mundane programming tasks from the main job market. Sophisticated automation of simpler programming tasks has led to a further reduction in the need for traditional programmers. A particularly popular approach has been to use interactive wizards in household appliances and entertainment devices. The fact that vendors sell many such software packages as integral parts of small, dedicated hardware appliances (rather than bits) further enhances software development's growing resemblance to traditional forms of engineering, such as civil and electrical. Such packaging dramatically increases the provable levels of many desirable system properties by resolving the physical-access bypass problem that has plagued software security since its origins. The creativity programming market uses many of the same languages as the late 1990s and early 2000s, of course, but with several surprises. Ada, once given up for dead by the US Department of Defense community that created it, has become the darling of the commercial-properties-composition industry. XML has continued to expand. Open source software has ceased to be controversial after businesses began to interpret it as an effective way to control the long-term support costs of the shared software infrastructure. The result is a much more stratified use of open source and proprietary software, with the widely shared infrastructure typically being open source. Finally, one of the more-interesting ongoing news items of 2010 has been the partial collapse of the international patent system, centering around claims of "patent feudalism" in software and biotechnology industries.

Using Harel's Statecharts to Model Business Workflows

In this paper, we model business workflows using Harel’s statecharts. Mapping to statecharts allows us to systematically identify potential workflow problems. It also allows us to investigate specific properties inherent in actual business workflows. Our research focuses on three desirable properties of active database systems — termination, confluence, and observable determinism. For termination and confluence, we develop algorithms to provide a theoretical lens linking desirable active database system properties to workflow management systems problems. We initially validate our algorithms by mapping business workflows from a case study. Our research thus builds preliminary theory by developing a systematic method for identifying workflow problems.

A CORBA extension for intelligent software environments

We describe the implementation of a technology that achieves system-wide properties in large software systems by controlling and modifying inter-component communications. Traditional component-based applications intermix the code for component functionality with support for systematic properties. This produces non-reusable components and inflexible systems. The Object Infrastructure Framework (OIF) separates systematic properties from functional code and provides a mechanism for weaving them together with functional components. This allows a much richer variety of component reuse and system evolution. Key elements of this technology include intercepting inter-component communications with discrete, dynamically configurable “injectors”, annotating communications and processes with additional meta-information, and a high-level, declarative specification language for describing the mapping between desired system properties and services that achieve these properties. We have implemented these ideas in a CORBA/Java framework for distributed computing, and are currently applying them to a distributed system for the analysis of aerospace design (wind-tunnel and CFD) data.